antonialachanc
/
qhdsx
1
0
Code Issues Packages Projects Wiki

Add DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart

Antonia Lachance 2025-02-13 16:33:41 +08:00
commit 66a9515179
1 changed files with 93 additions and 0 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

93
DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart.-.md Normal file

@ -0,0 +1,93 @@
<br>Today, we are excited to announce that DeepSeek R1 distilled Llama and Qwen models are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek [AI](https://www.anetastaffing.com)'s first-generation frontier model, DeepSeek-R1, together with the distilled versions ranging from 1.5 to 70 billion [specifications](https://naijascreen.com) to build, experiment, and [responsibly scale](http://1.15.187.67) your generative [AI](http://lohashanji.com) concepts on AWS.<br>
<br>In this post, we demonstrate how to begin with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar steps to deploy the distilled variations of the designs also.<br>
<br>Overview of DeepSeek-R1<br>
<br>DeepSeek-R1 is a large language model (LLM) established by DeepSeek [AI](https://linuxreviews.org) that uses reinforcement learning to improve thinking abilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. A crucial distinguishing function is its support knowing (RL) action, [wiki.myamens.com](http://wiki.myamens.com/index.php/User:TawnyaWhitley87) which was utilized to improve the design's actions beyond the standard pre-training and fine-tuning process. By integrating RL, [bytes-the-dust.com](https://bytes-the-dust.com/index.php/User:GinoHaley0) DeepSeek-R1 can adapt more efficiently to user feedback and goals, eventually boosting both importance and clearness. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) approach, indicating it's equipped to break down complex inquiries and factor through them in a detailed way. This assisted thinking process allows the design to produce more accurate, transparent, and detailed responses. This [design integrates](https://realestate.kctech.com.np) RL-based fine-tuning with CoT abilities, aiming to generate structured actions while concentrating on interpretability and user interaction. With its comprehensive abilities DeepSeek-R1 has actually captured the market's attention as a flexible text-generation model that can be incorporated into numerous workflows such as representatives, sensible reasoning and information analysis tasks.<br>
<br>DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture enables activation of 37 billion specifications, enabling effective reasoning by routing questions to the most pertinent specialist "clusters." This approach allows the design to concentrate on different problem domains while maintaining general effectiveness. DeepSeek-R1 requires a minimum of 800 GB of HBM memory in FP8 format for inference. In this post, we will utilize an ml.p5e.48 xlarge circumstances to deploy the design. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.<br>
<br>DeepSeek-R1 distilled designs bring the reasoning capabilities of the main R1 model to more [effective architectures](https://quickservicesrecruits.com) based on popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a procedure of training smaller, more effective models to imitate the habits and thinking patterns of the larger DeepSeek-R1 model, using it as a teacher design.<br>
<br>You can deploy DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we advise deploying this design with guardrails in place. In this blog site, we will utilize Amazon Bedrock Guardrails to present safeguards, avoid [harmful](https://kaamdekho.co.in) content, and evaluate designs against essential safety requirements. At the time of composing this blog, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create numerous guardrails tailored to various use cases and use them to the DeepSeek-R1 model, improving user experiences and standardizing safety controls throughout your generative [AI](https://git.lain.church) applications.<br>
<br>Prerequisites<br>
<br>To [release](http://git.cnibsp.com) the DeepSeek-R1 model, you need access to an ml.p5e circumstances. To check if you have quotas for P5e, open the Service Quotas console and under AWS Services, select Amazon SageMaker, and validate you're utilizing ml.p5e.48 xlarge for endpoint use. Make certain that you have at least one ml.P5e.48 xlarge instance in the AWS Region you are deploying. To ask for a limit boost, develop a limit boost request and reach out to your account team.<br>
<br>Because you will be [deploying](http://59.110.68.1623000) this model with Amazon Bedrock Guardrails, [engel-und-waisen.de](http://www.engel-und-waisen.de/index.php/Benutzer:Antonetta51H) make certain you have the proper AWS Identity and Gain Access To Management (IAM) approvals to utilize Amazon Bedrock Guardrails. For guidelines, see Set up consents to utilize guardrails for material filtering.<br>
<br>Implementing guardrails with the [ApplyGuardrail](http://szyg.work3000) API<br>
<br>Amazon Bedrock Guardrails allows you to introduce safeguards, avoid damaging content, and [assess models](https://thathwamasijobs.com) against key safety criteria. You can [implement](https://www.webthemes.ca) security procedures for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This permits you to apply guardrails to assess user inputs and design actions released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can create a guardrail using the Amazon Bedrock [console](https://gitlab.bzzndata.cn) or the API. For the example code to develop the guardrail, see the GitHub repo.<br>
<br>The basic circulation includes the following steps: First, the system gets an input for the design. This input is then processed through the ApplyGuardrail API. If the [input passes](https://gl.cooperatic.fr) the guardrail check, it's sent out to the model for inference. After getting the model's output, another guardrail check is applied. If the output passes this final check, it's [returned](https://www.iratechsolutions.com) as the result. However, if either the input or output is stepped in by the guardrail, a message is returned showing the nature of the intervention and whether it happened at the input or output phase. The examples showcased in the following areas demonstrate reasoning using this API.<br>
<br>Deploy DeepSeek-R1 in Amazon Bedrock Marketplace<br>
<br>Amazon Bedrock Marketplace offers you access to over 100 popular, emerging, and specialized foundation designs (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following steps:<br>
<br>1. On the Amazon Bedrock console, select Model catalog under Foundation designs in the navigation pane.
At the time of writing this post, you can use the InvokeModel API to conjure up the model. It does not support Converse APIs and other Amazon Bedrock [tooling](http://103.197.204.1623025).
2. Filter for DeepSeek as a provider and choose the DeepSeek-R1 design.<br>
<br>The design detail page offers essential details about the design's abilities, rates structure, and application standards. You can discover detailed use guidelines, including sample API calls and code snippets for combination. The design supports various text generation jobs, consisting of material creation, code generation, and concern answering, using its support learning optimization and CoT reasoning abilities.
The page likewise consists of implementation alternatives and licensing details to help you get begun with DeepSeek-R1 in your applications.
3. To start utilizing DeepSeek-R1, select Deploy.<br>
<br>You will be prompted to set up the implementation details for DeepSeek-R1. The design ID will be pre-populated.
4. For Endpoint name, enter an endpoint name (in between 1-50 alphanumeric characters).
5. For [Variety](https://hortpeople.com) of circumstances, enter a variety of circumstances (between 1-100).
6. For example type, select your instance type. For ideal efficiency with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is [advised](https://gitlab.profi.travel).
Optionally, you can set up advanced security and infrastructure settings, consisting of virtual private cloud (VPC) networking, service role permissions, and file [encryption settings](https://ratemywifey.com). For a lot of use cases, the default settings will work well. However, [higgledy-piggledy.xyz](https://higgledy-piggledy.xyz/index.php/User:LawerenceIsenber) for production releases, you might desire to review these settings to line up with your organization's security and compliance requirements.
7. Choose Deploy to begin using the design.<br>
<br>When the implementation is complete, you can check DeepSeek-R1's abilities straight in the Amazon Bedrock play ground.
8. Choose Open in playground to access an interactive user interface where you can experiment with various prompts and change design specifications like temperature level and maximum length.
When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for optimum results. For example, content for reasoning.<br>
<br>This is an excellent way to check out the design's thinking and text generation capabilities before integrating it into your applications. The playground offers immediate feedback, assisting you understand how the model reacts to various inputs and letting you tweak your prompts for optimal outcomes.<br>
<br>You can rapidly evaluate the model in the playground through the UI. However, to invoke the deployed design programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.<br>
<br>Run reasoning utilizing guardrails with the deployed DeepSeek-R1 endpoint<br>
<br>The following code example shows how to perform reasoning using a released DeepSeek-R1 design through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can produce a guardrail utilizing the Amazon Bedrock [console](https://viraltry.com) or the API. For the example code to create the guardrail, see the GitHub repo. After you have created the guardrail, utilize the following code to implement guardrails. The script initializes the bedrock_runtime customer, sets up inference parameters, and sends a request to generate text based upon a user timely.<br>
<br>Deploy DeepSeek-R1 with SageMaker JumpStart<br>
<br>SageMaker JumpStart is an artificial intelligence (ML) center with FMs, integrated algorithms, and prebuilt ML services that you can deploy with simply a couple of clicks. With [SageMaker](https://git.iovchinnikov.ru) JumpStart, you can tailor pre-trained designs to your use case, with your information, and deploy them into production utilizing either the UI or SDK.<br>
<br>Deploying DeepSeek-R1 model through SageMaker JumpStart provides 2 convenient methods: utilizing the instinctive SageMaker JumpStart UI or executing programmatically through the [SageMaker](https://tocgitlab.laiye.com) Python SDK. Let's explore both methods to assist you choose the method that best matches your needs.<br>
<br>Deploy DeepSeek-R1 through SageMaker JumpStart UI<br>
<br>Complete the following steps to release DeepSeek-R1 utilizing SageMaker JumpStart:<br>
<br>1. On the [SageMaker](https://newhopecareservices.com) console, pick Studio in the [navigation](https://kittelartscollege.com) pane.
2. [First-time](https://demo.shoudyhosting.com) users will be triggered to develop a domain.
3. On the SageMaker Studio console, select JumpStart in the navigation pane.<br>
<br>The model web browser shows available designs, with details like the service provider name and design capabilities.<br>
<br>4. Look for DeepSeek-R1 to view the DeepSeek-R1 design card.
Each design card reveals key details, consisting of:<br>
<br>- Model name
- Provider name
- Task category (for example, Text Generation).
Bedrock Ready badge (if appropriate), suggesting that this design can be registered with Amazon Bedrock, allowing you to utilize Amazon Bedrock APIs to invoke the model<br>
<br>5. Choose the model card to view the design details page.<br>
<br>The design details page consists of the following details:<br>
<br>- The model name and provider details.
Deploy button to release the design.
About and Notebooks tabs with detailed details<br>
<br>The About tab consists of essential details, such as:<br>
<br>- Model description.
- License details.
- Technical specifications.
- Usage standards<br>
<br>Before you deploy the design, it's suggested to examine the design details and license terms to validate compatibility with your usage case.<br>
<br>6. Choose Deploy to proceed with deployment.<br>
<br>7. For Endpoint name, use the automatically created name or develop a custom-made one.
8. For ¸ pick a circumstances type (default: ml.p5e.48 xlarge).
9. For Initial instance count, go into the number of circumstances (default: 1).
Selecting suitable circumstances types and counts is essential for cost and performance optimization. Monitor your release to change these settings as needed.Under Inference type, Real-time inference is chosen by default. This is optimized for sustained traffic and low latency.
10. Review all setups for accuracy. For this model, we highly suggest adhering to SageMaker JumpStart default settings and making certain that network isolation remains in place.
11. Choose Deploy to release the model.<br>
<br>The implementation procedure can take a number of minutes to complete.<br>
<br>When release is total, your endpoint status will change to InService. At this point, the design is all set to accept inference requests through the endpoint. You can keep an eye on the release progress on the SageMaker console Endpoints page, which will display relevant metrics and status details. When the implementation is complete, you can conjure up the design utilizing a [SageMaker runtime](http://gitlab.marcosurrey.de) client and incorporate it with your applications.<br>
<br>Deploy DeepSeek-R1 using the SageMaker Python SDK<br>
<br>To get going with DeepSeek-R1 utilizing the SageMaker Python SDK, you will require to install the SageMaker Python SDK and make certain you have the essential AWS approvals and environment setup. The following is a detailed code example that demonstrates how to deploy and utilize DeepSeek-R1 for reasoning programmatically. The code for deploying the model is offered in the Github here. You can clone the notebook and range from SageMaker Studio.<br>
<br>You can run extra requests against the predictor:<br>
<br>Implement guardrails and run inference with your SageMaker JumpStart predictor<br>
<br>Similar to Amazon Bedrock, you can also use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can develop a guardrail utilizing the Amazon Bedrock console or the API, and execute it as displayed in the following code:<br>
<br>Clean up<br>
<br>To avoid undesirable charges, finish the steps in this section to clean up your resources.<br>
<br>Delete the Amazon Bedrock Marketplace implementation<br>
<br>If you released the model using Amazon Bedrock Marketplace, total the following actions:<br>
<br>1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, pick Marketplace deployments.
2. In the Managed implementations section, locate the endpoint you want to erase.
3. Select the endpoint, and on the Actions menu, choose Delete.
4. Verify the endpoint details to make certain you're deleting the right implementation: 1. Endpoint name.
2. Model name.
3. Endpoint status<br>
<br>Delete the SageMaker JumpStart predictor<br>
<br>The SageMaker JumpStart model you released will sustain expenses if you leave it running. Use the following code to erase the endpoint if you want to stop sustaining charges. For more details, see Delete Endpoints and Resources.<br>
<br>Conclusion<br>
<br>In this post, we explored how you can access and release the DeepSeek-R1 model utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker [JumpStart](https://trabajosmexico.online) in SageMaker Studio or Amazon Bedrock Marketplace now to start. For more details, describe Use Amazon Bedrock tooling with [Amazon SageMaker](https://103.1.12.176) JumpStart models, SageMaker JumpStart pretrained designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting going with Amazon SageMaker JumpStart.<br>
<br>About the Authors<br>
<br>Vivek Gangasani is a Lead Specialist Solutions Architect for Inference at AWS. He assists emerging generative [AI](https://admithel.com) business develop ingenious options utilizing AWS services and accelerated calculate. Currently, he is focused on establishing techniques for fine-tuning and enhancing the inference efficiency of big language models. In his leisure time, Vivek enjoys treking, seeing motion pictures, and attempting various foods.<br>
<br>Niithiyn Vijeaswaran is a Generative [AI](https://www.srapo.com) Specialist Solutions Architect with the [Third-Party Model](http://git.guandanmaster.com) Science group at AWS. His area of focus is AWS [AI](https://git.fhlz.top) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.<br>
<br>Jonathan Evans is a Specialist Solutions Architect working on generative [AI](https://ckzink.com) with the Third-Party Model Science group at AWS.<br>
<br>[Banu Nagasundaram](https://myclassictv.com) leads product, engineering, and tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://winf.dhsh.de) hub. She is passionate about building services that assist consumers accelerate their [AI](https://euvisajobs.com) journey and unlock service worth.<br>